Deep Learning Driven QoS Anomaly Detection for Network Performance Optimization

Abstract

In modern, ever-changing network environments, QoS must be high to provide reliable and efficient services. This study tests Deep Learning (DL), specifically CNN, LSTM, and a hybrid CNN-LSTM model, to identify abnormalities using QoS measurements like Availability, Bandwidth, Latency, Jitter, and Packet Loss. The study evaluates DL-based QoS management using UNSW-NB15 data. The hybrid CNN-LSTM model excels at QoS management, identifying anomalies in key metrics with few false detections. This method captures intricate network data patterns and interrelationships using deep learning, improving anomaly detection accuracy and efficiency. A hybrid model is used to quantify QoS parameters like Availability, Bandwidth, Latency, Jitter, and Packet Loss. The results show high values for Packet Delivery Ratio (PDR), Throughput, Round-Trip Time (RTT), Variation in RTT, and Packet Loss Rate (PLR), proving the proposed approach's effectiveness in maintaining QoS. The CNN, LSTM, and suggested hybrid model evaluation metrics include Accuracy, Precision, Recall, and F1-Score. The hybrid model outperforms the individual models with 98.67% accuracy, precision, recall, and F1-Score. This proves its anomaly detection resilience. False Positive Rate and True Positive Rate show that the hybrid model performs best, with a 0.01 false positive rate and 0.98 true positive rate. Graphical representations help visualize DL model parameter comparisons and False Positive/True Positive rates. DL-based methods, particularly the hybrid CNN-LSTM model, are crucial for QoS anomaly detection in this study. Measurable results show the model improves network dependability, resource allocation, and user satisfaction. The study also suggests researching advanced deep learning methods for real-time network processing and scalable solutions.